A1 Refereed original research article in a scientific journal
A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer
Authors: Mason, Mike; Lapuente-Santana, Óscar; Halkola, Anni S.; Wang, Wenyu; Mall, Raghvendra; Xiao, Xu; Kaufman, Jacob; Fu, Jingxin; Pfeil, Jacob; Banerjee, Jineta; Chung, Verena; Chang, Han; Chasalow, Scott D.; Lin, Hung Ying; Chai, Rongrong; Yu, Thomas; Finotello, Francesca; Mirtti, Tuomas; Mäyränpää, Mikko I.; Bao, Jie; Verschuren, Emmy W.; Ahmed, Eiman I.; Ceccarelli, Michele; Miller, Lance D.; Monaco, Gianni; Hendrickx, Wouter R. L.; Sherif, Shimaa; Yang, Lin; Tang, Ming; Gu, Shengqing Stan; Zhang, Wubing; Zhang, Yi; Zeng, Zexian; Das Sahu, Avinash; Liu, Yang; Yang, Wenxian; Bedognetti, Davide; Tang, Jing; Eduati, Federica; Laajala, Teemu D.; Geese, William J.; Guinney, Justin; Szustakowski, Joseph D.; Vincent, Benjamin G.; Carbone, David P.
Publisher: BioMed Central
Publication year: 2024
Journal: Journal of Translational Medicine
Journal name in source: Journal of Translational Medicine
Article number: 190
Volume: 22
Issue: 1
eISSN: 1479-5876
DOI: https://doi.org/10.1186/s12967-023-04705-3
Publication's open availability at the time of reporting: Open Access
Publication channel's open availability : Open Access publication channel
Web address : https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-023-04705-3
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/387386055
Self-archived copy's licence: CC BY
Self-archived copy's version: Publisher`s PDF
Background
Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti–PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC.
Methods
Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials.
Results
A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression–based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1.
Conclusions
This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy.
Trial registration
CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.
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